我正在尝试为游戏蛇编写一个进化神经网络。我已经编码了神经网络部分，现在我想输出每一代最优秀的个人的游戏。为此，我使用的是图形库p5.js（https://p5js.org/）。
在我的代码中，我正在运行一个循环，在其中创建基于上一代的新一代。每一代人都必须玩游戏，这就是他们的评分方式。现在，我希望每个人都玩一次之后再输出最好的人。
在每个输出的最佳个人回合之间，我希望代码等待500毫秒。我该如何实现？
这是我已经尝试过的代码，但在这里，它仅在每一代的最后一轮之后才输出板子：
async function start() {
for (let i = 0; i < 50; i++) {
population.createNewGeneration();
let bestGameTurns = population.bestIndividual.game.turns; //Array of boards
for (let turn = 0; turn < bestGameTurns.length; turn++) {
let board = bestGameTurns[turn];
drawBoard(board);
let p = new Promise(resolve => setTimeout(resolve, 500));
await p;
function drawBoard(board) {
//Draw the board using p5.js rect()'s
}
}
}
}
另一个版本，但是等待在这里无效：
let i = 0;
setInterval(async () => {
population.createNewGeneration();
console.log(i, population.avgFitness);
let bestGameTurns = population.bestIndividual.game.turns; //Array of boards
for (let turn = 0; turn < bestGameTurns.length; turn++) {
let board = bestGameTurns[turn];
drawBoard(board);
let p = new Promise(resolve => setTimeout(resolve, 500));
await p;
function drawBoard(board) {
//Draw the board using p5.js rect()'s
}
}
i++;
}, 1);
答案 0 :(得分：0)
您可以这样创建一个简短函数：
function pause(ms) {
return new Promise((resolve) => setTimeout(resolve, ms));
}
然后在任何async函数中，您都可以这样称呼它：
async function () {}
// something happening
await pause(500);
// continue
}
现在，您问题中的代码尚不完整，因此是一种盲目编码。
因此，首先setInterval将每1毫秒（实际上是4毫秒，因为这是JS中的最小值）运行整个函数。这意味着它将运行这些循环。我决定专注于您标记的部分。
我要求循环而不是尝试暂停它，而不是尝试暂停它，而不是尝试循环？
另外，我将drawBoard移到
setInterval(async () => {
// ^^^^^^^^ <-- this should probably go
population.createNewGeneration();
console.log(i, population.avgFitness);
let bestGameTurns = population.bestIndividual.game.turns; //Array of boards
function tick(turn = 0) {
let board = bestGameTurns[turn];
function drawBoard(board) {
//Draw the board using p5.js rect()'s
}
drawBoard(board);
// here is "setTimeouted" loop
if (turn < bestGameTurns.length) {
setTimeout(tick, 500, turn + 1);
}
}
tick();
}, 1);
答案 1 :(得分：0)
您提供的代码应该可以满足您的要求，我只能为您整理一些部分。更好地说明您面临的问题。
// The function should be defined only once.
function drawBoard(board) { }
async function start() {
for (let i = 0; i < 50; i++) {
population.createNewGeneration();
const bestGameTurns = population.bestIndividual.game.turns; //Array of boards
for (let turn = 0; turn < bestGameTurns.length; turn++) {
// Don't wait on first iteration
await new Promise(resolve => setTimeout(resolve, 500 * (turn ? 0 : 1 )));
drawBoard(bestGameTurns[turn]);
}
}
}
答案 2 :(得分：0)
感谢大家，您的建议使我有了一个主意。我发现问题出在其他地方。因为javascript只在一个线程上运行（我想这就是它的调用方式），所以在运行了一代之后，我们必须停止该函数，以让运行每帧的另一个draw函数绘制电路板。绘制后，主要功能可以继续。看起来是这样：
let isDrawn = false;
let currentBoard;
async function setup() {
for (let i = 0; i < 50; i++) {
population.createNewGeneration();
const bestGameTurns = population.bestIndividual.game.turns;
for (let turn = 0; turn < bestGameTurns.length; turn++) {
await step(bestGameTurns[turn], turn);
}
}
}
function step(board, turn) {
currentBoard = board;
isDrawn = false;
return new Promise(resolve => setTimeout(() => {
if (isDrawn) resolve();
}, 500));
}
setTimeout(() => {
if (currentBoard) {
drawBoard(currentBoard);
isDrawn = true;
currentBoard = undefined;
}
}, 1);
const nrOfCols = 10;
const nrOfRows = 10;
const fieldWidth = 20;
const nodeNrs = [24, 8, 8, 4];
const populationSize = 200;
const mutationRate = 0.01;
let population;
let game;
let isDrawn = false;
let currentBoard;
async function setup() {
createCanvas(500, 500);
population = new PopulationManager(populationSize);
for (let i = 0; i < 50; i++) {
population.createNewGeneration();
const bestGameTurns = population.bestIndividual.game.turns;
for (let turn = 0; turn < bestGameTurns.length; turn++) {
await step(bestGameTurns[turn]);
}
}
}
function step(board) {
currentBoard = board;
isDrawn = false;
return new Promise(resolve => setTimeout(() => {
if (isDrawn) resolve();
}, 500));
}
function draw() {
if (currentBoard) {
drawBoard(currentBoard);
isDrawn = true;
currentBoard = undefined;
}
}
function drawBoard(board) {
board.forEach((col, colNr) => {
col.forEach((field, rowNr) => {
fill(field.isSnake ? "green" : field.isFruit ? "red" : "black");
stroke(255);
rect(colNr*fieldWidth, rowNr*fieldWidth, fieldWidth, fieldWidth);
});
});
}
function play(game) {
setInterval(() => {
if (!game.lost) {
game.nextTurn();
drawBoard(game.board);
} else {
clearInterval(1);
}
}, 500);
}
class PopulationManager {
constructor(populationSize) {
this.population = createPopulation();
function createPopulation() {
let population = [];
for (let i = 0; i < populationSize; i++) {
let chromosomes = createRandomChromosomes();
let i = new Individual(chromosomes);
population.push(i);
}
return population;
function createRandomChromosomes() {
let arr = [];
let nrOfChromosomes = calcNrOfChromosomes();
for (let i = 0; i < nrOfChromosomes; i++)
arr.push(Math.random()*2-1);
return arr;
function calcNrOfChromosomes() {
let nr = 0;
for (let i = 0; i < nodeNrs.length - 1; i++)
nr += (nodeNrs[i] + 1)*nodeNrs[i + 1];
return nr;
}
}
};
}
createNewGeneration() {
let that = this;
getFitnessOfPop();
this.calcAvgFitness();
this.findBestIndividual();
let parents = selection();
breed(parents);
function getFitnessOfPop() {
that.population.forEach(iv => {
iv.fitness = iv.playGame();
});
that.population.sort((a, b) => a.fitness - b.fitness);
}
function selection() {
let totalFitness = that.population.map(iv => iv.fitness/* + 1 */).reduce((a,b) => a + b);
let allParents = [];
for (let i = 0; i < that.population.length/2; i++) {
allParents.push(selectRandomParents());
}
return allParents;
function selectRandomParents() {
let p1, p2;
do {
p1 = selectRandomParent();
p2 = selectRandomParent();
} while (p1 == p2);
return [p1, p2];
function selectRandomParent() {
let rdm = Math.random()*totalFitness;
return that.population.find((iv, i) => {
let sum = that.population.filter((iv2, i2) => i2 <= i).map(iv => iv.fitness /* + 1 */).reduce((a,b) => a + b);
return rdm <= sum;
});
}
}
}
function breed(allParents) {
that.population = [];
allParents.forEach(ps => {
let childChromosomes = crossOver(ps[0].chromosome, ps[1].chromosome);
childChromosomes = [mutate(childChromosomes[0]), mutate(childChromosomes[1])];
let child1 = new Individual(childChromosomes[0]);
let child2 = new Individual(childChromosomes[1]);
that.population.push(child1);
that.population.push(child2);
});
function crossOver(parent1Chromosome, parent2Chromosome) {
let crossingPoint = Math.round(Math.random()*parent1Chromosome.length);
let divided1 = divideChromosome(parent1Chromosome);
let divided2 = divideChromosome(parent2Chromosome);
let child1Chromosome = divided1[0].concat(divided2[1]);
let child2Chromosome = divided2[0].concat(divided1[1]);
return [child1Chromosome, child2Chromosome];
function divideChromosome(chromosome) {
let part1 = chromosome.filter((g, i) => i <= crossingPoint);
let part2 = chromosome.filter((g, i) => i > crossingPoint);
return [part1, part2];
}
}
function mutate(chromosome) {
chromosome = chromosome.map(g => {
if (Math.random() < mutationRate)
return Math.random()*2-1;
return g;
});
return chromosome;
}
}
}
calcAvgFitness() {
this.avgFitness = this.population.map(iv => iv.fitness).reduce((a, b) => a + b) / this.population.length;
}
findBestIndividual() {
let bestFitness = -1, bestIndividual;
this.population.forEach(i => {
if (i.fitness > bestFitness) {
bestFitness = i.fitness;
bestIndividual = i;
}
});
this.bestIndividual = bestIndividual;
}
}
class Individual {
constructor(chromosome) {
this.chromosome = chromosome;
this.fitness = 0;
this.game = createGame();
function createGame() {
let weights = convertChromosomeToWeights();
let game = new Game(weights);
return game;
function convertChromosomeToWeights() {
let weights = [];
for (let i = 0; i < nodeNrs.length - 1; i++) {
let lArr = [];
for (let j = 0; j < nodeNrs[i] + 1; j++) {
let nArr = [];
lArr.push(nArr);
}
weights.push(lArr);
}
chromosome.forEach((gene, geneIdx) => {
let lIdx = -1, minIdx, maxIdx = 0;
for (let i = 0; i < nodeNrs.length - 1; i++) {
let nr = 0;
for (let j = 0; j <= i; j++)
nr += (nodeNrs[j] + 1)*nodeNrs[j + 1];
if (geneIdx < nr) {
lIdx = i;
break;
}
maxIdx = nr;
minIdx = maxIdx;
}
minIdx = maxIdx;
let nIdx = -1;
for (let i = 0; i < nodeNrs[lIdx] + 1; i++) {
let nr = minIdx + nodeNrs[lIdx + 1];;
if (geneIdx < nr) {
nIdx = i;
break;
}
maxIdx = nr;
minIdx = maxIdx;
}
minIdx = maxIdx;
let wIdx = -1;
for (let i = 0; i < nodeNrs[lIdx + 1]; i++) {
let nr = minIdx + 1;
if (geneIdx < nr) {
wIdx = i;
break;
}
maxIdx = nr;
minIdx = maxIdx;
}
weights[lIdx][nIdx][wIdx] = gene;
});
return weights;
}
}
}
playGame() {
while (!this.game.lost) {
this.game.nextTurn();
}
return this.game.score;
}
}
class Game {
constructor(weights) {
let that = this;
this.chromosome = flattenArray(weights);
this.nn = new NeuralNetwork(weights);
this.turnNr = 0;
this.score = 0;
this.lost = false;
this.board = createBoard();
this.snake = new Snake();
setupSnake();
this.createFruit();
this.turns = [JSON.parse(JSON.stringify(this.board))];
function createBoard() {
let board = [];
for (let colNr = 0; colNr < nrOfCols; colNr++) {
board[colNr] = [];
for (let rowNr = 0; rowNr < nrOfRows; rowNr++) {
let field = new Field(colNr, rowNr);
board[colNr][rowNr] = field;
}
}
return board;
}
function setupSnake() {
for (let i = 0; i < 4; i++)
that.addToTail([floor(nrOfCols/2) - i, floor(nrOfRows/2)]);
that.length = that.snake.body.length;
}
function flattenArray(arr) {
let flattened = [];
flatten(arr);
return flattened;
function flatten(arr) {
arr.forEach(e => {
if (Array.isArray(e))
flatten(e);
else
flattened.push(e);
});
}
}
}
addToTail(pos) {
this.snake.body.push(pos);
this.board[pos[0]][pos[1]].setSnake(true);
}
nextTurn() {
let that = this;
let direction = findDirection();
this.moveSnake(direction);
this.turns.push(JSON.parse(JSON.stringify(this.board)));
this.turnNr++;
checkEat();
function findDirection() {
let inputValues = [];
for (let i = 0; i < 8; i++) {
let distances = that.snake.look(i, that.board);
inputValues.push(distances.distToFruit);
inputValues.push(distances.distToWall);
inputValues.push(distances.distToBody);
}
let output = that.nn.getOutput(inputValues);
let probability = -1;
let direction = -1;
output.forEach((v, vIdx) => {
if (v > probability) {
probability = v;
direction = vIdx;
}
});
return direction;
}
function checkEat() {
let head = that.snake.body[0];
let headField = that.board[head[0]][head[1]];
if (headField.isFruit) {
that.snake.eat();
that.score++;
headField.setFruit(false);
that.createFruit();
}
}
}
createFruit() {
let field;
do {
let colNr = floor(random()*nrOfCols);
let rowNr = floor(random()*nrOfRows);
field = this.board[colNr][rowNr];
} while(field.isSnake);
field.setFruit(true);
}
moveSnake(newDirection) {
let that = this;
let oldBody = JSON.parse(JSON.stringify(that.snake.body));
moveTail();
makeSnakeLonger();
moveHead();
function moveTail() {
for (let i = oldBody.length - 1; i > 0; i--)
that.snake.body[i] = oldBody[i - 1];
}
function moveHead() {
let newHeadPosition = findNewHeadPos();
if (
newHeadPosition[0] >= nrOfCols || newHeadPosition[0] < 0 ||
newHeadPosition[1] >= nrOfRows || newHeadPosition[1] < 0
) {
that.lose();
return;
}
let newHeadField = that.board[newHeadPosition[0]][newHeadPosition[1]];
if (newHeadField.isSnake) {
that.lose();
return;
}
addNewHeadPos(newHeadPosition);
}
function findNewHeadPos() {
if (newDirection == 0) { //up
return [oldBody[0][0], oldBody[0][1] - 1];
} else if (newDirection == 1) { //right
return [oldBody[0][0] + 1, oldBody[0][1]];
} else if (newDirection == 2) { //down
return [oldBody[0][0], oldBody[0][1] + 1];
} else if (newDirection == 3) { //left
return [oldBody[0][0] - 1, oldBody[0][1]];
}
}
function makeSnakeLonger() {
if (that.snake.length > that.snake.body.length) {
that.addToTail(oldBody[oldBody.length - 1]);
} else {
removeFromTail(oldBody[oldBody.length - 1]);
}
}
function removeFromTail(pos) {
that.board[pos[0]][pos[1]].setSnake(false);
}
function addNewHeadPos(pos) {
that.snake.body[0] = pos;
that.board[pos[0]][pos[1]].setSnake(true);
}
}
lose() {
this.lost = true;
}
}
class Field {
constructor(col, row) {
this.col = col;
this.row = row;
this.isFruit = false;
this.isSnake = false;
}
setFruit(bool) {
this.isFruit = bool;
}
setSnake(bool) {
this.isSnake = bool;
}
}
class Snake {
constructor() {
this.body = [];
this.length = 4;
}
eat() {
this.length++;
}
look(direction, board) {
let distances = {distToFruit: 0, distToWall: 0, distToBody: 0};
let xDiff = getXDiff(direction), yDiff = getYDiff(direction);
let pos = [this.body[0][0] + xDiff, this.body[0][1] + yDiff];
let dist = 1;
while (pos[0] > 0 && pos[0] < nrOfRows && pos[1] > 0 && pos[1] < nrOfCols) {
if (board[pos[0]][pos[1]].isFruit && distances.distToFruit == 0) distances.distToFruit = dist;
if (board[pos[0]][pos[1]].isSnake && distances.distToBody == 0) distances.distToBody = dist;
pos[0] += xDiff, pos[1] += yDiff;
dist++;
}
distances.distToWall = dist;
return distances;
function getXDiff(direction) {
if (direction == 5 || direction == 6 || direction == 7) return -1;
else if (direction == 1 || direction == 2 || direction == 3) return 1;
return 0;
}
function getYDiff(direction) {
if (direction == 7 || direction == 0 || direction == 1) return -1;
else if (direction == 3 || direction == 4 || direction == 5) return 1;
return 0;
}
}
}
class NeuralNetwork {
constructor(weights) {
this.layers = createLayers();
this.layers = addWeights(this.layers, weights);
function createLayers() {
let layers = [];
let nrOfNodesGlobal;
nodeNrs.forEach((nrOfNodes, lNr) => {
nrOfNodesGlobal = nrOfNodes;
layers[lNr] = [];
for (let i = 0; i < nrOfNodes; i++) {
let node = createNode(lNr);
layers[lNr][i] = node;
}
if (lNr != nodeNrs.length - 1)
layers[lNr].push(new Bias());
});
return layers;
function createNode(lNr) {
if (lNr == 0) return new InputLayerNode();
else if (lNr == nrOfNodesGlobal - 1) return new OutputLayerNode();
else return new HiddenLayerNode();
}
}
function addWeights(layers, weights) {
for (let lNr = 0; lNr < layers.length - 1; lNr++) {
let l = layers[lNr];
l.forEach((n1, nNr) => {
for (let n2Nr = 0; n2Nr < layers[lNr+1].length - 1; n2Nr++) { //not including bias of next layer
let n2 = layers[lNr+1][n2Nr];
let weight = weights[lNr][nNr][n2Nr];
let w = new Weight(n1, n2, weight);
n1.addWeight(w);
}
});
}
return layers;
}
}
getOutput(inputValues) {
let output = [];
this.layers[0].forEach((inputNeuron, nNr) => {
if (nNr != this.layers[0].length - 1)
inputNeuron.addToInput(inputValues[nNr]);
});
this.layers.forEach((l, lNr) => {
calcOutputs(l);
if (lNr != this.layers.length - 1) {
l.forEach(n => {
n.feedForward();
});
} else {
output = l.map(n => n.output);
}
});
return output;
function calcOutputs(layer) {
layer.forEach(n => n.output = n.activationFunction(n.summedInput, layer.map(N => N.summedInput)));
}
}
log() {
console.log(this.weights, this.nodes);
}
}
class Node {
constructor() {
this.weights = [];
this.summedInput = 0;
}
addWeight(w) {
this.weights.push(w);
}
addToInput(input) {
if (input == NaN)
console.log("A");
this.summedInput += input;
}
feedForward() {
this.weights.forEach((w, wNr) => {
let input = w.weight*this.output;
w.to.addToInput(input);
});
}
}
class Bias extends Node {
constructor() {
super();
this.output = 1;
}
activationFunction(x, allXs) {
return x;
}
}
class InputLayerNode extends Node {
constructor() {
super();
}
activationFunction(x, allXs) {
return x;
}
}
class HiddenLayerNode extends Node {
constructor() {
super();
}
activationFunction(x, allXs) {
return leakyReLU(x);
}
}
class OutputLayerNode extends Node {
constructor() {
super();
}
activationFunction(x, allXs) {
return softmax(x, allXs);
}
}
class Weight {
constructor(from, to, weight) {
this.from = from;
this.to = to;
this.weight = weight;
}
setWeight(newWeight) {
this.weight = weight;
}
}
function leakyReLU(x) {
if (x >= 0) return x;
else return 0.01*x;
}
function softmax(x, allXs) {
return Math.exp(x) / allXs.map(X => Math.exp(X)).reduce((a, b) => a+b);
}<!DOCTYPE html>
<html>
<head>
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.8.0/p5.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.8.0/addons/p5.dom.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.8.0/addons/p5.sound.min.js"></script>
<link rel="stylesheet" type="text/css" href="style.css">
<meta charset="utf-8" />
</head>
<body>
<script src="sketch.js"></script>
</body>
</html>
效果不佳，但应该进行一些改进才能使它变得更好...
如果您对代码的改进有任何建议，请告诉我！
答案 3 :(得分：-1)
我尝试按照评论中的说明将其修复为步骤，希望我没有错：
let i = 0;
async function step(bestGameTurns, turn)
{
if (turn == bestGameTurns.length)
return;
let board = bestGameTurns[turn];
drawBoard(board);
let p = new Promise(resolve => setTimeout(() => step(bestGameTurns, turn+1), 500));
await p;
}
function drawBoard(board) {
//Draw the board using p5.js rect()'s
}
setInterval(async () => {
population.createNewGeneration();
console.log(i, population.avgFitness);
let bestGameTurns = population.bestIndividual.game.turns; //Array of boards
step(bestGameTurns, 0);
i++;
}, 1);